Generative AI and Retrieval-Augmented Generation (RAG) in Electrical and Computer Engineering

Abstract

Generative AI and Retrieval-Augmented Generation (RAG) technologies are revolutionizing electrical and computer engineering by streamlining research, development, and simulation workflows. This survey comprehensively explores their applications in power systems, high-voltage direct current (HVDC) technologies, hardware design, and Internet of Things (IoT) systems. Aimed at senior engineers in India, the paper highlights use cases, benefits, challenges, and implementation strategies, including a SWOT analysis and a comparison between proprietary and open-source solutions.

1. Introduction

The field of electrical and computer engineering is undergoing a paradigm shift with the integration of Generative AI and Retrieval-Augmented Generation (RAG) systems. These technologies harness large language models (LLMs) and structured retrieval pipelines to automate and augment engineering tasks. From accelerating power grid simulation to optimizing printed circuit board (PCB) design and enabling predictive maintenance in IoT ecosystems, AI-driven systems are delivering measurable improvements in speed, cost-efficiency, and decision-making accuracy.

2. Applications in Power Systems and HVDC

2.1 Grid Simulation and Optimization

  • Scenario Modeling: MIT researchers use generative models to simulate grid loads and solar adoption impacts, aiding infrastructure planning.
  • Generative Load Forecasting: AI models trained on historical usage and weather data create multiple grid demand scenarios to stress-test power systems.

2.2 Predictive Maintenance

  • Failure Prediction: RAG-LLMs analyze maintenance logs and SCADA data to identify high-risk equipment.
  • Work Order Automation: AI suggests actions from retrieved past maintenance records, enabling faster decision-making.

2.3 HVDC Systems

  • Standards Compliance: RAG systems ingest IEEE standards and grid codes to assist in HVDC project compliance.
  • Real-Time Fault Diagnosis: Integration with fault logs enables AI to recommend responses to HVDC converter station issues.

3. Applications in Hardware Development

3.1 PCB and System Design

  • Automated Schematics: ChatGPT and similar tools are used for first-draft PCB designs, achieving up to 25% time savings.
  • Code & Documentation: AI accelerates the writing of firmware and design documentation.

3.2 Component Selection

  • Context-Aware Datasheet Queries: RAG-LLMs extract relevant parameters from indexed datasheets.
  • Bill of Materials (BOM) Optimization: AI recommends alternative components based on availability and pricing.

3.3 Verification and Validation

  • Code Review: 47% of engineers identify minor bugs in AI-generated designs, requiring integrated validation tools.
  • Hardware-in-the-Loop Simulation: AI is integrated with simulators for design verification.

4. Applications in IoT and Embedded Systems

4.1 Synthetic Data Generation

  • Training ML Models: Generative AI creates synthetic datasets for anomaly detection and predictive maintenance.

4.2 Edge AI and Real-Time Systems

  • Latency Optimization: On-device AI inference enables sub-10ms response times in industrial automation.
  • Smart Grid Devices: IoT nodes equipped with AI predict load peaks, reducing energy waste by 15–20%.

4.3 RAG-Enhanced IoT Workflows

  • API Automation: LLMs augmented with sensor documentation generate API workflows automatically.
  • Manual-Free Maintenance: Integration of sensor readings with maintenance guides facilitates AI-driven troubleshooting.

5. Implementation Strategies

5.1 Proprietary vs. Open-Source RAG

FactorOpenAI’s GPTLLaMA (Open Source)
Customization Prompt-only Fine-tuning available
Cost Subscription Local infra cost
Security Cloud-based On-prem deployment
Use Case Rapid prototyping Regulated, sensitive domains

5.2 Recommended Tech Stack

  • Vector DBs: FAISS, Pinecone, Weaviate
  • LLMs: GPT-4, Claude, Mistral, LLaMA 3
  • Frameworks: LangChain, Haystack, RAGFlow

6. Use Cases in India

6.1 Smart City Projects

  • Energy Efficiency: Predictive grid control using AI in Indian cities such as Pune and Bhubaneswar.
  • Public Infrastructure: Real-time AI monitoring for transformers and substations in rural electrification.

6.2 HVDC Substations

  • Project Management: RAG-LLMs streamline technical documentation in PowerGrid projects.
  • Training Simulation: AI-generated scenarios for engineer training at NTPC and BHEL facilities.

6.3 IoT in Agriculture and Industry

  • Smart Irrigation: Generative AI models predict soil moisture and automate watering.
  • Process Automation: AI deployed in PLC-based systems in automotive manufacturing.

7. SWOT Analysis

FactorStrengthsWeaknessesOpportunitiesThreats
Generative AI Boosts productivity, lowers cost Errors in complex designs Customization for local use Over-reliance on AI
RAG Systems Domain-specific intelligence High setup complexity Regulatory compliance Data privacy concerns
India Context Large engineer base Skill gaps in AI/ML Global market positioning Import reliance for AI hardware

8. Challenges and Future Directions

8.1 Data Quality and Validation

  • Issue: 20% of engineers report major issues with AI-generated outputs.
  • Solution: Incorporate automated verification and test benches.

8.2 Upskilling Engineers

  • Issue: Junior engineers risk obsolescence.
  • Solution: Structured AI literacy and upskilling programs.

8.3 Localization and Deployment

  • Issue: Models are often trained on Western datasets.
  • Solution: Train on local data and deploy on BharatNet and NIC cloud infrastructure.

9. Recommended Resources

Books

  1. Generative AI in Practice – 100+ Use Cases
  2. Prompt Engineering for Generative AI
  3. Fundamentals of Generative AI

Research Papers

  1. “Developing RAG Systems with PDF Documents” – arXiv (2023)
  2. “Generative AI for Grid Planning” – MIT LIDS
  3. “IoT and Generative AI: Predictive Maintenance” – Entrans (2024)

Websites

  1. AWS RAG Guide
  2. DNV Power Sector AI
  3. BairesDev AI Survey

10. Conclusion

Generative AI and RAG are transforming the electrical and computer engineering landscape in India. From optimizing power infrastructure to designing smarter embedded systems and automating documentation, these technologies enhance productivity and reduce operational risks. As adoption scales, Indian engineers must prioritize training, data governance, and context-specific model development to maximize benefits sustainably.

Acknowledgments

Special thanks to ongoing research from MIT, NREL, and industry leaders like AWS, OpenAI, and Entrans for shaping the global dialogue on AI for engineering.

Author Contributions

Tapas Shome – Research, writing, and integration of global and India-specific insights.

About the Author

Tapas Shome is a senior electrical and computer engineer from India, affiliated with IAS-Research.com and KeenComputer.com. His work focuses on AI integration in engineering systems, with a mission to bridge digital transformation and sustainable infrastructure in emerging markets.

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